We develop a theory for solving continuous time optimal stopping problems for non-linear expectations. Our motivation is to consider problems in which the stopper uses risk measures to evaluate future rewards. Our development is presented in two parts. In the first part, we will develop the...

We develop a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption. Log-earnings are the sum of a general Markovian persistent component and a transitory innovation. The persistence of past shocks to earnings is...

The reaction coefficients in the forecast-based monetary policy reaction function are only weakly identified when the smoothing coefficient for the nominal interest rate is close to unity. This situation also causes the nominal interest rate to be highly persistent. Inference based on the...

Motivated by the desire to probe macroeconomic tail events and to capture non-linear economic dynamics, we estimate two types of regime switching models: threshold VAR and Markov switching VAR. For each of the models, we estimate regimes which carry the interpretation of recessionary/normal and...

We propose an estimation strategy that accounts for two major problems raised in the empirical literature testing for the prevalence of the inverted U-shaped relation between environmental degradation and economic activity, namely the Environmental Kuznets Curve (EKC) hypothesis. First, we use...

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing...

We analyse the dynamics of public debt in Serbia between 2004Q3 and 2014Q3. Our results are as follows: i) traditional unit root tests do not reject the unit root hypothesis for public debt; ii) unit root tests robust to sample size and high AR (1) coefficient provide mixed evidence with respect...